{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Support Vector Machine with Randomized Search\n",
    "SVM is trained on feature set 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Get the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "X_16_val                  -> array([[ 3.00880939,  0.26443017,  1.05370334, ...\n",
      "X_32_val                  -> array([[-0.13964146,  0.53184264, -0.71694033, ...\n",
      "X_32test_std              -> defaultdict(<class 'list'>, {0: array([[-0.1396414\n",
      "X_32train_std             -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "X_test                    -> defaultdict(<class 'list'>, {0: array([[[-0.006215\n",
      "X_test_std                -> defaultdict(<class 'list'>, {0: array([[ 3.0088093\n",
      "X_train                   -> array([[[-0.01174874, -0.00817356, -0.0042913 , ..\n",
      "X_train_std               -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "snrs                      -> [-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, \n",
      "y_16_val                  -> array([3, 5, 1, ..., 2, 4, 0])\n",
      "y_32_test                 -> defaultdict(<class 'list'>, {0: array([2, 2, 3, ..\n",
      "y_32_train                -> array([5, 0, 2, ..., 4, 6, 6])\n",
      "y_32_val                  -> array([2, 2, 3, ..., 0, 3, 5])\n",
      "y_test                    -> defaultdict(<class 'list'>, {0: array([3, 5, 1, ..\n",
      "y_train                   -> array([5, 0, 2, ..., 4, 6, 6])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from time import time\n",
    "import pickle  \n",
    "import sklearn\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "%store -r\n",
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:  (80000, 16) and labels:  (80000,)\n",
      " \n",
      "Test data:\n",
      "Total 20 (4000, 16) arrays for SNR values:\n",
      "[-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data: \", X_train_std.shape, \"and labels: \", y_train.shape)\n",
    "print(\" \")\n",
    "print(\"Test data:\")\n",
    "print(\"Total\", len(X_test_std), X_test_std[18].shape, \"arrays for SNR values:\")\n",
    "print(sorted(X_test_std.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and test the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 350.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randomized search took 356.00 minutes \n",
      "   \n",
      "Result of randomized search, best estimator:\n",
      "SVC(C=10.0, cache_size=7000, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.10000000000000001,\n",
      "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
      "  shrinking=True, tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "#Train the classifier\n",
    "\n",
    "from sklearn import svm\n",
    "\n",
    "params = {'C': np.logspace(-3, 2, 6), 'gamma': np.logspace(-3, 2, 6)}\n",
    "\n",
    "rand_search_cv = RandomizedSearchCV(svm.SVC(cache_size = 7000), params, verbose=1)\n",
    "\n",
    "start = time()\n",
    "rand_search_cv.fit(X_train_std, y_train)\n",
    "print(\"Randomized search took %.2f minutes \"%((time() - start)//60))\n",
    "print(\"   \")\n",
    "print(\"Result of randomized search, best estimator:\")\n",
    "print(rand_search_cv.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test the classifier\n",
      " \n",
      "SVM's accuracy on -20 dB SNR samples =  0.12375\n",
      "SVM's accuracy on -18 dB SNR samples =  0.12325\n",
      "SVM's accuracy on -16 dB SNR samples =  0.132\n",
      "SVM's accuracy on -14 dB SNR samples =  0.12875\n",
      "SVM's accuracy on -12 dB SNR samples =  0.1535\n",
      "SVM's accuracy on -10 dB SNR samples =  0.183\n",
      "SVM's accuracy on -8 dB SNR samples =  0.27425\n",
      "SVM's accuracy on -6 dB SNR samples =  0.356\n",
      "SVM's accuracy on -4 dB SNR samples =  0.409\n",
      "SVM's accuracy on -2 dB SNR samples =  0.386\n",
      "SVM's accuracy on 0 dB SNR samples =  0.42325\n",
      "SVM's accuracy on 2 dB SNR samples =  0.572\n",
      "SVM's accuracy on 4 dB SNR samples =  0.73425\n",
      "SVM's accuracy on 6 dB SNR samples =  0.772\n",
      "SVM's accuracy on 8 dB SNR samples =  0.7695\n",
      "SVM's accuracy on 10 dB SNR samples =  0.78675\n",
      "SVM's accuracy on 12 dB SNR samples =  0.78175\n",
      "SVM's accuracy on 14 dB SNR samples =  0.7845\n",
      "SVM's accuracy on 16 dB SNR samples =  0.768\n",
      "SVM's accuracy on 18 dB SNR samples =  0.78525\n"
     ]
    }
   ],
   "source": [
    "#Test the classifier\n",
    "\n",
    "import collections\n",
    "\n",
    "y_pred = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "\n",
    "print(\"Test the classifier\")\n",
    "print(\" \")\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = rand_search_cv.predict(X_test_std[snr])\n",
    "    accuracy[snr] = metrics.accuracy_score(y_test[snr], y_pred[snr])\n",
    "    print(\"SVM's accuracy on %d dB SNR samples = \"%(snr), accuracy[snr])   \n",
    "    \n",
    "accuracy = collections.OrderedDict(sorted(accuracy.items()))  #sort by ascending SNR value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Visualize classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fCV9VrYRQ0woJhYWFuH79Ok6fPo1Tp06pFbi2trbV3pVM1eNXcQMDA6uc0NShQwe0atVK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\nREREusOQS0RERES68xNM9in3pUB+7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c2801c93c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4), dpi=100)\n",
    "x = snrs\n",
    "y = list(accuracy.values())\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Without Normalization\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK    411     2      0     0     3     38     37    25\n",
      "BPSK      0   481      0     0    10      2     10     3\n",
      "CPFSK    27     1    458    92     1      2      0    13\n",
      "GFSK      8     1     42   408     3      4     16     7\n",
      "PAM4      1    14      0     0   483      6      2     0\n",
      "QAM16    15     1      0     0     0    174    121    21\n",
      "QAM64     9     0      0     0     0    244    302     7\n",
      "QPSK     29     0      0     0     0     30     12   424\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "y_predicted = rand_search_cv.predict(X_test_std[18])\n",
    "conf_matrix = confusion_matrix(y_predicted, y_test[18]) \n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix Without Normalization\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.80  0.00   0.00  0.00  0.01   0.07   0.07  0.05\n",
      "BPSK   0.00  0.95   0.00  0.00  0.02   0.00   0.02  0.01\n",
      "CPFSK  0.05  0.00   0.77  0.15  0.00   0.00   0.00  0.02\n",
      "GFSK   0.02  0.00   0.09  0.83  0.01   0.01   0.03  0.01\n",
      "PAM4   0.00  0.03   0.00  0.00  0.95   0.01   0.00  0.00\n",
      "QAM16  0.05  0.00   0.00  0.00  0.00   0.52   0.36  0.06\n",
      "QAM64  0.02  0.00   0.00  0.00  0.00   0.43   0.54  0.01\n",
      "QPSK   0.06  0.00   0.00  0.00  0.00   0.06   0.02  0.86\n"
     ]
    },
    {
     "data": {
      "image/png": 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R8Y9sNNA6EfFc0anfA52yRIGZmbW2zhpo09VqmZLRD1heCIiS1gP2B2ZGxL8a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\nRD5ry6IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1554606400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normalize the matrix\n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8, 4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['svm3.pkl']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "\n",
    "joblib.dump(rand_search_cv, \"svm3.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.externals import joblib\n",
    "rand_search_cv = joblib.load(\"svm3.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
